{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、分析问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.月收入以及月收入增长  \n",
    "2.月活跃客户,月订单数量，平均订单收入，新客户比例,月留存率以及同期群留存  \n",
    "3.客户分群\n",
    "4.预测客户的终生价值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、数据来源及字段理解"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据由kaggle提供，包含8个字段，分别是国家编码，订单编号，订单日期，描述，数量，单价，国家，股票代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('OnlineRetail.csv',encoding='ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      "InvoiceNo      541909 non-null object\n",
      "StockCode      541909 non-null object\n",
      "Description    540455 non-null object\n",
      "Quantity       541909 non-null int64\n",
      "InvoiceDate    541909 non-null object\n",
      "UnitPrice      541909 non-null float64\n",
      "CustomerID     406829 non-null float64\n",
      "Country        541909 non-null object\n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "      InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/2010 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/2010 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "      <td>406829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.552250</td>\n",
       "      <td>4.611114</td>\n",
       "      <td>15287.690570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.081158</td>\n",
       "      <td>96.759853</td>\n",
       "      <td>1713.600303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>-11062.060000</td>\n",
       "      <td>12346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13953.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>15152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.130000</td>\n",
       "      <td>16791.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice     CustomerID\n",
       "count  541909.000000  541909.000000  406829.000000\n",
       "mean        9.552250       4.611114   15287.690570\n",
       "std       218.081158      96.759853    1713.600303\n",
       "min    -80995.000000  -11062.060000   12346.000000\n",
       "25%         1.000000       1.250000   13953.000000\n",
       "50%         3.000000       2.080000   15152.000000\n",
       "75%        10.000000       4.130000   16791.000000\n",
       "max     80995.000000   38970.000000   18287.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "关于订单数量负值比较多，需要和具体部门沟通，是否是数据错误，这里暂时只处理离群比较远的点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      "InvoiceNo      541909 non-null object\n",
      "StockCode      541909 non-null object\n",
      "Description    540455 non-null object\n",
      "Quantity       541909 non-null int64\n",
      "InvoiceDate    541909 non-null datetime64[ns]\n",
      "UnitPrice      541909 non-null float64\n",
      "CustomerID     406829 non-null float64\n",
      "Country        541909 non-null object\n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "      <td>406829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.552250</td>\n",
       "      <td>4.611114</td>\n",
       "      <td>15287.690570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.081158</td>\n",
       "      <td>96.759853</td>\n",
       "      <td>1713.600303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>-11062.060000</td>\n",
       "      <td>12346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13953.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>15152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.130000</td>\n",
       "      <td>16791.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice     CustomerID\n",
       "count  541909.000000  541909.000000  406829.000000\n",
       "mean        9.552250       4.611114   15287.690570\n",
       "std       218.081158      96.759853    1713.600303\n",
       "min    -80995.000000  -11062.060000   12346.000000\n",
       "25%         1.000000       1.250000   13953.000000\n",
       "50%         3.000000       2.080000   15152.000000\n",
       "75%        10.000000       4.130000   16791.000000\n",
       "max     80995.000000   38970.000000   18287.000000"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**重复值、异常值、缺失值处理**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 8)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleanData = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(536641, 8)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleanData.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleanData = cleanData[(cleanData['Quantity'] > 0)&(cleanData['UnitPrice'] > 0) & (cleanData['CustomerID'].notnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**日期格式转换**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleanData['InvoiceDate']=pd.to_datetime(cleanData['InvoiceDate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(392692, 8)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleanData.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、数据处理及指标分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**月收入**  \n",
    "月收入=活跃用户数量 * 订单数量 * 平均订单价格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceYearMonth</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201012</td>\n",
       "      <td>570422.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201101</td>\n",
       "      <td>568101.310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201102</td>\n",
       "      <td>446084.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201103</td>\n",
       "      <td>594081.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201104</td>\n",
       "      <td>468374.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201105</td>\n",
       "      <td>677355.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201106</td>\n",
       "      <td>660046.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>201107</td>\n",
       "      <td>598962.901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>201108</td>\n",
       "      <td>644051.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>201109</td>\n",
       "      <td>950690.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>201110</td>\n",
       "      <td>1035642.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>201111</td>\n",
       "      <td>1156205.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>201112</td>\n",
       "      <td>517190.440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    InvoiceYearMonth      Revenue\n",
       "0             201012   570422.730\n",
       "1             201101   568101.310\n",
       "2             201102   446084.920\n",
       "3             201103   594081.760\n",
       "4             201104   468374.331\n",
       "5             201105   677355.150\n",
       "6             201106   660046.050\n",
       "7             201107   598962.901\n",
       "8             201108   644051.040\n",
       "9             201109   950690.202\n",
       "10            201110  1035642.450\n",
       "11            201111  1156205.610\n",
       "12            201112   517190.440"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleanData['InvoiceYearMonth'] = cleanData['InvoiceDate'].map(lambda date: 100*date.year + date.month)\n",
    " \n",
    "cleanData['Revenue'] = cleanData['UnitPrice'] * cleanData['Quantity']\n",
    "revenue = cleanData.groupby(['InvoiceYearMonth'])['Revenue'].sum().reset_index()\n",
    "revenue\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '销售额')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4),dpi=80)\n",
    "plt.plot(revenue['InvoiceYearMonth'].astype(str),revenue['Revenue'],marker='o')\n",
    "plt.title('月销售额')\n",
    "plt.xlabel('月份')\n",
    "plt.ylabel('销售额')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2011年12月由于数据不完整出现收入下降情况，来查看月收入增长率情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pct_change()查看利润增长率\n",
    "revenue['MonthlyGrowth'] = revenue['Revenue'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1a818660e10>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4),dpi=80)\n",
    "plt.plot(revenue.loc[0:11,'InvoiceYearMonth'].astype('str'),revenue.loc[0:11,'MonthlyGrowth'],marker='o')\n",
    "plt.title('月销售增长率')\n",
    "plt.xlabel('月份')\n",
    "plt.ylabel('增长率')\n",
    "plt.axhline(color='red',linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "关于201104月增长率为下降比较快进行分析   \n",
    "分析一:各个渠道的来源是否正常？进行各个渠道的数据进行查看分析，分析渠道转化率\n",
    "分析二:是否是进行了促销  \n",
    "分析三:细分客户，分析新客户的收入和老客户的复购  \n",
    "分析四:是否产品进行功能改版导致流失用户"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**月活跃客户**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里只关注记录数最多的国家，也就是英国，来查看每月的活跃用户数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "uk = cleanData[cleanData['Country']=='United Kingdom'].reset_index(drop=True)\n",
    "monthly_active = uk.groupby('InvoiceYearMonth')['CustomerID'].nunique().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vMuLF/wflK1K7fR8yBk0m59Qfk9H8IirVO5Nd/7eEeXV/yl1/L/p82cM6ldr3kvhTkSoiIiJH5MROv8rfTr/t5UL7nNTl3gPaqp2VScWTfkDuxmxOuLgX5SpVSVpGOfqoSBUREZHIVDqxPpVOrH/ojlLmpOSaVDOraGZTzCwzfN/VzBaY2Rwze8rMLGx/yMw+DvumhW1pZjbXzN43swdSkVdEREREopX0ItXMKgFvA/XD91WAHkCmu7cFfgS0MrMrgNZAC+BR4KHwEE8CL7r7hUCGmbVOdmYRERERiVaqVvf3BhYDuPsud7/a3XeaWXnge8B64DLgVXffB8wGLgg/eynwl3D7baBDwYOb2UAzW5v32r59e5K/joiIiIgkU9KLVHff7e5ri9j9W2C2u/8DqAF8FX7GgWphn13uvjPc3gLULeQcI909Pe9VvXr10v0SIiIiKZKbm0vHjh3JysrKb5s1axaXXXZZof27devG2LFj89+7Ox06dGDOnDlJTiqSXJHdJ9XMOgJXAQPDpq38tzAFqBn+d29CWw3Akp9OREQk9Xbv3k3nzp33e4b9m2++ydChQ/nuu+8O6D9+/Hjeeuut/dpGjBhBkyZNaNu2bdLziiRTJEWqmV0EPAxcnTBKOp9gah8zOxPYGLZ/ZmYtwu0WwJpUZhUREUml5557jhYtWuS/P/fcc3nhhRcO6Ld+/XpGjBhB375989s+/fRThg4dSu3atZk0aVJK8ookS1QjqeOAKsB4M8sys04E15u2MrMngfHAY2Hf4cDLZjYS6A68EkVgERGRZKtUqRLp6en7tZ122mmF9r311lt57LHHqFGjRn7bwIEDueuuu7j11luZM2cODzygm+LI0StlRaq793L3rHC7nrv/0N0zw9dkd98FXAjMBH7u7s+Efd8DOgILgfPdfV2qMouIiMTR888/T6NGjbjwwgv3a//oo4/47W9/S1paGrfccgvTpk2LKKHIkYvVzfzDQnViIe3ZQHaq84iIiMTRxIkT2bx5M5mZmWRnZ1OlShVq1arFmWeeyerVq2nUqBHz5s2jQYMGUUcVOWyxKlJFRETk0N5555387cGDB5ORkUGXLl049dRT6devH7m5ueTm5jJu3LgIU4ocGQvu9nRsSU9P97Vri7rrlYiISHQyBk2OOkK+7GGdDtlHeQ9fcfKWZWb2tbunF7U/sltQiYiIiIgURUWqiIiIiMSOilQRERERiR0VqSIiIiISOypSRURERCR2VKSKiIiISOyoSBURERGR2FGRKiIiIiKxoyJVRERERGJHRaqIiIiIxI6KVBERERGJHRWpIiIiIhI7KlJFREREJHZUpIqIiIhI7KhIFREREZHYUZEqIiIiIrGjIlVEREREYkdFqoiIiIjEjopUEREREYmdlBSpZlbRzKaYWWb4/gwzW2hm882sT0K/fmb2qZnNMrPTw7YqZjbZzOaZ2Rgzs1RkFhEREZHoJL1INbNKwNtA/YTmV4BBQBugq5nVN7OmwG1AK6AP8HjYdwjwkbu3ATYA1yc7s4gc/TZs2EDXrl1p164d7du3Z8WKFQDs3LmTFi1asHLlSgC2b9/OVVddxY9//GPOO+88Fi1aFGVsEREJpWq6vzewGMDMjgdOcfdZ7u7AdKAdcCkw0d13uvsqoJ6ZlQcuA14Oj/M20CFFmUXkKHbffffRuXNnZs+ezW9+8xv69esHwK9//WtuvPFGzjjjDABeeeUVrr/+embMmMHDDz/MfffdF2VsEREJJb1Idffd7r42oakG8FXC+y1A3ULac4C0Au15ffdjZgPNbG3ea/v27aX5FUTkKLR06VJatmwJQEZGBps3b2bq1KmMHz+eXbt2MXv2bAD69u3L9dcHEzTr16+nXr16kWUWEZH/imLh1FagWsL7GoAVsz2vbT/uPtLd0/Ne1atXT0pwETl69OjRgyFDhjBnzhwGDhxIjx496N+/PyNHjuSmm27imWee4eWXX87vv3HjRh566CGNpIqIxETKi1R33wJgZnkjoi2ANcB8gil/zKwmUAfYmNie0FdE5KD69+/PXXfdxcyZM1m5ciXdu3cnNzeXnj17UqdOHW688UamTZsGwO7du7nuuut48MEHadCgQcTJRUQEoEJE530EeMfMFgBnA73dPcfMypnZWKAB8Ky77zGzJ4C3zKwl0A24MqLMInKUadasGXfeeSejR4/m5JNPplKlSmzcuJG0tDTmzZtHgwYN2Lt3L926daNTp0507do16sgiIhJKWZHq7r0Stl8zs0+ApsAgd88Jd3UGLgd2uvussO9yM7uIYHHVKHfPTlVmETm6jR8/nrp169KhQ7DectSoUVxzzTXk5uZSvXp13njjDZ5//nmmTJnCpk2bePvtt0lLS+ONN96IOLmIiEQ1koq7fwF8UaBtLzC5kL4bgNdTFE1EYi5j0AF/TRShGtS7bv/+bQblbzZ/ZD5wCnXu+H9kh23ZJTo+ZA/rVOy+IiJSfHrilIiIiIjEjopUEREREYkdFakiIiIiEjsqUkVEYmDbtm1cffXVtGnThpYtW7Js2TJef/11WrduTdOmTfPv31rU415FRI41kS2cEhGR/3rppZc499xz+d3vfse0adMYPHgwH374IcuXL6dq1apccMEF3HDDDYwcOZLOnTvTs2dPpk+fTr9+/Zg5c2bU8UVESp1GUkUiUtyRM4B7772Xc845h44dO7Jx48YIU0uy1KlThxUrVpCTk8OiRYs466yzqF27NsuXL2ft2rVs3bqVevXqFfq4VxGRY5FGUkUiUtyRs+zsbObPn8/ixYvJysri3nvv5Zlnnok6vpSyiy66iFdffZUnnniCv//97zzwwANUr16dl156iSpVqtCxY0dq1qyZ/7jXW2+9lUceeYQePXpEHV1EJClUpIpEpE6dOnzwwQf7jZytXLmS5cuXk5aWlj9yNmbMGG644QbKlStHu3bt+OUvfxl1dEmC3/zmNwwePJhmzZqxY8cOTj75ZDp06MCECRMAGDRoEBMmTKB///5cdNFFTJgwgZUrV+bvFxE51mi6XyQiF110ETt27MgfOfvJT35Cjx49eOmll3jyySfzR862bdtG/fr1ATAzcnJyDnFkORrl5OTw8ccfA/Dee++xfft2li9fzo4dO9i5cycLFizAzIDgca/z589n9OjRVK5cOZK8hV2uMmbMGBo3bkxmZiaZmZmsXr2anJwcbr75Ztq1a0fbtm354IMPIskrIkcfjaSKRKS4I2c1a9bcrzDdunVrVJElie6//3569+7N7bffTt26dfnb3/7GjBkzyMjI4LvvvuPKK6+kc+fOwIGPe41CYZerVKxYkXHjxtGkSZP8fg899BAZGRm88MILrFixgo4dO/Lll19GlltEjh4aSRWJSHFHzlq3bp2/envFihWkpaVFGVuS5Oyzz2bx4sVs376dVatW0b59e4YPH84333zDli1bePXVV6lYsSIA1157LS+//HKkeQtb6PX+++9z++2307x5cwYMGIC777fQq379+mzZsiXS3CJy9NBIqkhEijtytnfvXh555BH69+/P3Llz+dWvfhV1dCmhjEGTo46QL3tYp1I5TmELvZo2bco111xDbm4uV1xxBVlZWVx//fWMHDmS4447jhdeeIEbbrihVM5fWj7//HPWrVvHBRdcQPXq1aOOIyIJil2kmtmvgP91932F7LsSWOzu60sznMjRqthFSfshnNgecoHe734H5S7muJsv5jhgHnD6/TMA8Mx7Gb9mMeWb3cTDa07h4RIUPaVVlIgkKni5SqtWrVi4cCEAFStWpGnTpixbtox+/frRuHFjpk6dyqRJkyJ7+MC2bdvo0aMH//73v9mzZw9jx47lgw8+4K9//SvNmzfnjjvu4MMPP6RGjRr5n+nWrRuXX345vXr1iiSzSFlXkun+nwDvm9lfzWy4mf3UzGqYWUPgT0CV5EQUEatQiePOaE3lemdGHUUEOPByleXLl3PZZZeRm5vLtm3bmDZtGueeey4ADRs2ZMmSJQwfPjyyy1XyrqGdN28eQ4YMYfDgwXz11VdMmDCBIUOG8MMf/pAlS5bk9x8/fjxvvfVWJFlFJHDIkVQzq+Due4B9wEXAScCpQGtgCUGh29Xds5OYU0REYqTg5SpTpkxh7ty5/PCHP6RatWrcdttt+deiLliwgNWrV0d6f9/Cbvn2+9//npycHCZNmsS6devyi+r169czYsQI+vbtG1leESnedP97ZrYNyABaAt8HzgPaAu8QFKmtgYVJynjMKGy6qVGjRsyaNYtHHnmE6dOn5/dt2LAh6enpAFx44YUMHTo0qtgiUsYc9uUqnA9dz2cr8OhaeDTxOC3v5LS7p5Q4SzKvoQVYt24d48aN46STTsq/xdett97KY489xrvvvlsq5xaRw3PIItXd25hZXaAvMBDoCLwGXOjue8ysHPCumU1291XJjXt0K+yWLV27dmX06NHs2/ffS31Xr17N2WefzZtvvhlhWhGRY0dh19AuWbKE008/nfHjx9O9e3emTJnCpk2baNSoERdeeKGKVJGIHfKaVDN7FOgJtAcGAa8Dk4BpZtYN+CPwMnB8EnMeEwq7Zcu5557LCy+8sF+/OXPm8PHHH9OmTRsuuOACFi1aFFFiEZFjQ8FraN2dJk2a8M033wDwn//8hxNOOIGJEycyd+5cMjMzGTt2LMOGDWPixIlRRhcps4oz3f8swVT/NcCvgEnuPtnMBgNnAL2AU9x9R5IyHjMKm2467bTTyM7O3q9fs2bNmDFjBg0bNmT27NnceeedvPfee9GEFhE5BhS8hvapp54iJyeHK664gqpVq+Y/JSszMzP/M4MHDyYjI4MuXbpEF1ykDCtOkToc+A5oARwH1DWzqwEHJgIXAI8C/ZIV8lhR1HRTQY0bN6ZKleBmCeeccw7Lli1LdVSRAzz//PO88sor+e8XLlzI888/zxNPPJH/iMyC107rFj6SCod/DW0F6BBcm/rnPfDnA45zHiyHwbrlm0gkinMLqvuAxcAyYAPB1P7jBAuo3gCGAVXMLCM5EY8dBaebitKrV6/8RVTjxo2jRYsWKckncjC33HILWVlZZGVl8Yc//IEuXbowaNAgZs6cydKlS5k5c+Z+/6DSLXxERORIFGck9WzgRYIR0x7Ae0BnYDVwOcEoa0WgEZCdlJTHiMKmmwrz4IMP0q1bN+68807S09N5+umnU5xU5ODuvvtuXnnlFa666iqWL19OWloaW7dupV69eoBu4SMiIkeuOKv78+b3ugCYWaa7bye4Z2qeEi2BNLMawJ+BE8MMvYC9Ydse4CV3HxP27Qf0Ab4F+sTxDgIleuRhwemmd8PPtvrN/scJp6CWARc/9RnwWbFPoekmSaaZM2dyxhln8P3vf58ePXrw0ksvUaVKFTp27EjNmjUB3cJHRESOXEkei3o6sBtwM0sD/uPumw/zvDcBH7n7A2Z2OTCYYHHWIGA28DczmwrUAm4juB42neAygysO85wiUgoef/xxBg8ezJdffsm8efOYMGECAIMGDWLChAls3rxZt/AREZEjVuwiFfiUYIrfCArKnxPcjupwrAcuMLNqBA8G+AJo4+6zAMxsOtAOOAGY6O47gVVmVs/Myrv73sSDmdlAgnu4AlCrVq3DjCUiB7Nx40bWrFlD8+bNWbp0KcuXL2fHjh2YGQsWLOD8889n4sSJbN68mczMTLKzs6lSpQq1atXSCmkRESmRkhSpK9y9OYCZ/d3dD7dABZgL3ADcDjQHHgMuS9i/BahLcK3rVwntOUAaQZGbz91HAiPz3qenp/sRZBORIkydOpVLLrkEgKZNm3LllVeSkZHBd999x5VXXknnzp255ppr8vvrFj4iInK4SlKkehHbh2MEMNjdPzWz44DPga0J+2sQjNhuBaoV0i4iEejZsyc9e/bMfz98+HCGDx9eZP/BgwenIJWIiByLDlmkmtk1QP1SPm814ByCSwguBraF56rr7v8iuAb1r8Aa4PfAY2ZWE6gDbCzlLCJlXokW/yWZFv6JiAgcokg1s3IEt5l6geDRqKXlQeA5M3sC+BfQFzgJeMfMFhDc9qq3u+eYWTkzGws0AJ519z2lmENEREREYuigRfOpLqIAABVeSURBVKq77wN+AWBWerPs7v4JwWjpfszsE6ApMMjdc8LmzgSF8s68hVUiIiIicmwryTWplc3s+wRPqapkZlXDVfelxt2/IFjpn9i2F4jPXKSIiIiIJF1JitR/AWOAfQTXhS4xsx3A88Az7r4rCflEREREpAwqdpHq7u0LtpnZD4BfA5+YWRNdLyoiIiIipaEkI6kHcPc1wG1mdpYKVBEREREpLcW5BdWDwB6Kvjfqf4DnSjOUiIiIiJRt5YrR51rgH8D/Ad3C/3YHssPtVsCtSconUmJjx46ld+/e7N27l8zMzPzXWWedRZ8+fdiwYQNdu3alXbt2tG/fnhUrVkQdWURERAooznT/N+7+MoCZ3eTuL5lZ74S2BUClZIYUKa41a9bwxz/+kXnz5lG+fHmysrLy93Xu3JkBAwZw33330blzZ3r27Mn06dPp168fM2fOjC60iIiIHKA4I6mFTfMntq0CPiudOCKHb9++ffTo0YPGjRvz8ssvs2nTpvx9M2bMID09ncaNG7N06VJatmwJQEZGBps3b44qsoiIiBShOEXqeWa2xsz+AbQyszUF2v4B3JjUlCLF8Oqrr7Jv3z6GDx9O48aNyczMZN++fQAMGzaMQYMGAdCjRw+GDBnCnDlzGDhwID169IgytoiIiBSiONP9i939IgAzm+3u7cxsbl6bSFwsXLiQPn36UL9+ferXr0+VKlVYvXo1O3bsoEaNGpx66qkA9O/fn4suuogJEyawcuVKJkyYEHFyERERKaik0/1eSJsc4/IWIiVaunQpZ555JgCTJ0/eb4FS7dq1+eCDD1Ke86yzzspfBLVx40b++c9/csoppzB27Fi6d+++X99mzZoxf/58Ro8eTeXKlVOeVURERA6uOCOpZ5jZC4ABjcLtM8JbUy0GZpT241ElPhIXIuXJzc2lT58+fPfddwB06tSJTp06AZCdnc0tt9xCq1atUp71Zz/7GX379uXiiy/mm2++YdiwYVSrVo133nmH+++/f7++48ePp27dunTo0CHlOUVEROTQilOk/hTYTTB6+iTB6OsLwPeBi4ERZvYndx+dtJQSiYILkbp3707t2rUZPHgw3bt359FHHz3gM/fccw8PP/wwZpbyvFWqVOHFF188oH316tUHtF177bVce+21qYglIiIih+GQRaq7v3+Q3X8xs98B6aUXSeIicSHSl19+SWZmJs8++yyffPIJQ4cOPaBIXblyJZs2bcpfOV9aMgZNLtXjHansYZ2ijiAiInLMO6LHogK4+3ZgeSlkkZgpuBCpcuXKdOnShcWLFxc6Ujpq1Cj69OkTQVIRERE51hRn4ZSUUQUXIq1du5bKlSvTvXt3MjMzWb9+PVdffTUAe/bs4Z133sm/NlVERETkSBzxSKocuwpbiHTTTTfl78/IyGDSpEkAzJ8/nyZNmlCpkh4+JiIiIkdORaoUqaiFSHmys7Pzt9u2bUvbtm1TkEpERETKAhWpZZAWIomIiEjc6ZpUEREREYkdFakiIiIiEjsqUkVEREQkdlSkioiIiEjsRFqkmlkvM3su3D7DzBaa2Xwz65PQp5+ZfWpms8zs9OjSioiIiEiqRFakmtkPgF8DA8OmV4BBQBugq5nVN7OmwG1AK6AP8HgUWUVERESi1LBhQzIzM8nMzOS+++4DwN3p0KEDc+bMye83evRomjVrxiWXXMKqVauiilsqIrkFlZmVA/4MfA70NLPXgFPcfVa4fzrQDjgBmOjuO4FVZlbPzMq7+94CxxvIf4tdatWqlaJvIiIiIpJcq1ev5uyzz+bNN9/cr33EiBE0adIk/z7lS5cuZdSoUSxevJi1a9cyYMAApk6dGkXkUhHVSOoN4bl/S1CofgR8lbB/C1AXqFGgPQdIK3gwdx/p7ul5r+rVqyctuIiIiEgqzZkzh48//pg2bdpwwQUXsGjRIj799FOGDh1K7dq185/+OHPmTLp06ULVqlU5/fTTWbduHXv37j3E0eMrqpv5nw+McfevgK/MbDPQPGF/DcCArUC1QtpFREREyoRmzZoxY8YMGjZsyOzZs7nzzjupUKECd911F7/4xS8YNmwYn376KWZG/fr18z9XrVo1Nm7cSJ06dSJMf/iiGkn9AjgTwMzSgDrAWjOrG+5vAawB5gOXhv1qhv02pjytiIiISEQaN25Mw4YNATjnnHNYtmwZH330Eb/97W9JS0vjlltuYdq0adSsWZOcnJz8z23btg13jyr2EYuqSH0RONnM3gPmECyY+j3wjpmNBs4G3nH3RUA5MxsLTAaedfc9EWUWERERSblevXoxffp0AMaNG0eLFi0488wzWb16NQDz5s2jQYMGtG7dmpkzZwKwdetW1q9fT1raAVdJHjUime53913Azwq2m9knQFNgkLvn/VOgM3A5sDNvYZWIiIhIWfHggw/SrVs37rzzTtLT03n66af59ttv6devH7m5ueTm5jJu3DgyMjLYt28fvXr14ssvv+TnP/85FSpEdWXnkYtVcnf/guBSgMS2vQSjqCIiIiLHlIxBxSxxOjwAwDLg4qc+C9pa3pm/O/Ppz4HP8Sa3svgfH2PpZ/DPfc14rbjHB7KHdSp231SIVZEqIiIiIofPypXnuAbnRR2jVOixqCIiIiISOypSRURERCR2VKSKiIiISOyoSBURERGR2FGRKiIiIiKxoyJVRERERGJHRaqIiIiIxI6KVBERERGJHRWpIiIiIhI7KlJFREREJHZUpIqIiIhI7KhIFREREZHYUZEqIiIiIrGjIlVEREREYkdFqoiIiIjEjopUEREREYkdFakiIiIiEjsqUkVEREQkdlSkioiIiEjsqEgVERERkdiJvEg1s9fNrJeZpZnZXDN738weSNjfxcw+Dfe1ijKriIiIiKRGpEWqmV0LXBW+fRJ40d0vBDLMrLWZnQT8CbgUuBp4PJqkIiIiIpJKkRWpZlYH+A3wVNh0KfCXcPttoAPQGpjr7v92903ARjM7tZBjDTSztXmv7du3p+AbiIiIiEiyRDmS+jTwK2Bb+H6Xu+8Mt7cAdYEawFcJn8lr34+7j3T39LxX9erVkxhbRERERJItkiLVzG4Blrn7+wnNexO2awAGbAWqFdIuIiIiIsewChGdtwtwvJllARnALuBEM2vh7ouBFsAaYAEwxMyMoKBuBvwjksQiIiIikjKRFKnufmXetpkNBrIJis+XzWwa8BPgAndfb2YfABMIsr7v7utTn1hEREREUimqkdR87j44b9vMOgKtgEfcfUO4v6+ZXQJUBaZGElJEREREUiryIjWRu2cTjKoWbJ+V8jAiIiIiEpnIb+YvIiIiIlKQilQRERERiR0VqSIiIiISOypSRURERCR2VKSKiIiISOyoSBURERGR2FGRKiIiIiKxoyJVRERERGJHRaqIiIiIxI6KVBERERGJHRWpIiIiIhI7KlJFREREJHZUpIqIiIhI7KhIFREREZHYUZEqIiIiIrGjIlVEREREYkdFqoiIiIjEjopUEREREYkdFakiIiIiEjsqUkVEREQkdlSkioiIiEjsqEgVERERkdiJpEg1s+pm9paZzTCzRWZ2npmdYWYLzWy+mfVJ6NvPzD41s1lmdnoUeUVEREQktSpEdN4bgb+4+1/MrAMwFDgeGATMBv5mZlOBWsBtQAsgHXgcuCKayCIiIiKSKpEUqe7+VMLbOsA6oLG7zwIws+lAO+AEYKK77wRWmVk9Myvv7nsTj2dmA4GBee9r1aqV7K8gIiIiIkkU6TWpZpYG3AuMBb5K2LUFqAvUKNCeA6QVPI67j3T39LxX9erVkxdaRERERJIusiLVzCoB44D7gU+Aagm7awAGbC2iXURERESOYVEtnCoPvA5Mdvc33H1L2F437NICWAPMBy4N99UkuDRgY+oTi4iIiEgqRbVw6hagI1DbzDoTFJ6PAO+Y2QLgbKC3u+eYWTkzGws0AJ519z0RZRYRERGRFIlq4dQzwDMF283sE6ApMMjdc8LmzsDlwM68hVUiIiIicmyLaiS1UO7+BfBFgba9wORoEomIiIhIFPTEKRERERGJHRWpIiIiIhI7KlJFREREJHZUpIqIiIhI7KhIFREREZHYUZEqIiIiIrGjIlVEREREYkdFqoiIiIjEjopUEREREYkdFakiIiIiEjsqUkVEREQkdlSkioiIiEjsqEgVERERkdhRkSoiIiIisaMiVURERERiR0WqiIiIiMSOilQRERERiR0VqSIiIiISOypSRURERCR2VKSKiIiISOyoSBURERGR2DkqilQze8jMPjazKWaWFnUeEREREUmu2BepZnYF0BpoATwKPBRtIhERERFJNnP3qDMclJn9CfjM3Z8zMwOWuHvTAn0GAgMTmuoA61MYs7RUB7ZHHaIElDe5lDe5lDe5lDe5lDe5lDc10ty9clE7K6QyyWGqAXwF4O5uZtUKdnD3kcDIVAcrbWa21t3To85RXMqbXMqbXMqbXMqbXMqbXMobD7Gf7ge2AomFac2ogoiIiIhIahwNRep84FIAMzsT2BhtHBERERFJtqOhSH0baGVmTwLjgccizpNMR9slC8qbXMqbXMqbXMqbXMqbXMobA7FfOAVgZlWAK4Cv3X1h1HlEREREJLmOiiJVRERERMqWo2G6X0RERETKGBWpIiIiIhI7KlJFjhFmVtfMrjCzelFnEREROVIqUkuRmVU3s7fMbIaZLTKz88zsDDNbaGbzzaxPQl8zs2fNrFdCW5qZzTWz983sgQLHvs/MBie8bxz2m2FmM82sdqrzhu1NzOzvBdr6mdmnZjbLzE5PaD/FzLJLmjOKvEfbz9fMmgITgbOB6WZ2fpzzJuyrZmarzSwjznnN7Ewz+9rMssLXT+OcN2HfWDPrUdKsqc5rZk8k/Gznm9mKmOdtambvhed5y8yOi3ne883sb2Hm18yseozyHtDXDvK7MI55i+ob17xmdrGZfWjB77Y3LVicHk/urlcpvYC+wPXhdgdgOrAAuAQw4F2gfrj/L8BioFfC58cBN4fbLwOtw+0RwAfA4IS+LwANw+2HgLsjyHshMAXITmhrCnwOVAVOB6aG7WcCM4FNEf58S5L3aPv5/gxoEG7fDtwb57wJ+0cDO4CMOOcFegP9I/77oUQ/X6ArMPFoyZvQZxDQJ855gTeBtuH2MKBvzPMuA34Qbv8BuD8OeQ/St9DfhTHOW2jfGOedAHwv3H4177xxfGkktRS5+1Pu/pfwbR1gHXCKu8/y4E/DdKBduP8e4J0Ch7iU4A8UBPeH7RBujwbGFDjXze6+usC5Up33K6BzId9horvvdPdVQD0zKw9sAa4keILYYUll3qPt5+vuLwJrzOxC4Drgr3HOC2BmPwbSgMO6rVyK87YFeoUjHFPM7HtxzmvBJR9PAyvM7Ia8n3lc8+btDH+uVwPPxjzveqCJmdUgKAyXxThvNaCWu68J+2QDx8ckb1F9i/pdGNe8RfWNZV53v8bdvy1wrlhSkZoEZpYG3AuMJfiDk2cLUBfA3f9RyEd3ufvOYvbNO9eFwDnAa6nO6+5fufveAs01ChwjB0hz9/UJ3+2IpCJvwrmOip9vuF2JoECtErbHNq+ZHQ8MBW493JypzEswspPp7ucRjEoMiHneB8PMI4GTOYyiL8V589wBPOHu+2KedxzBP1wGEPzD+0imeZOdtwYwz8x+Y2aXA78mGD2LQ94S/S6Ma96i+sY1b8K5rgd2u/t7Rxg7aSpEHeBYY2aVCP4Cux/4BKiWsLsGwbB9URL/gB2qL2Z2KsEI6/+4e24EeQuztRSOUaRU5j3afr7u/h1wu5n9AvgV8MsY530c+J27f2t2+H88Uph3prvvCts+Am6Ied7zgavc/RszexpYXdiHY5Q371w/AZodTtYU530CaO7uHhZ+Qwkus4lr3m4ERfVtwAJ3/7ikWZOUtygl+l1YlBTmLRWpzGtm5xCMsl5SWsdMBo2klqJwGuh1YLK7v+HuW8L2vH8FtgDWFPV54DMza1GcvmZWB5hEcO3WqojyFmY+wVQNZlaTYCph4+HkizLv0fbzNbPxZnZp2Kc28J8Y590GtAYGmVkWwWKv183stJjm/TfwsZmdbEFFfS3BaGqJpPj/ty8IrgMHaAN8GfO8AB2B947gH4SpzFsP+EH45+FioMRPxUll3nCE7e8Elyb8uqRZk5i3KMX+XViUFOc9YqnMa2aNCK71/am7l8rv52TRSGrpuoXgL9raZtaZ4C+zR4B3zGwBwS/j3gf5/HDgZTObRjCicMFB+o4k+AtoaDgS9Td3fyjFeQ/g7ovMrJyZjQUaAM+6+54S5oo8r5kdVT9fM7sfeN7Mfgd8A9xcwqypzLsdaJjXJyxUe7l7dkzz5prZ3cBc4DuCQuB/S5g1lXn3mNkg4CkzuweoGZ47tnnD3VcRXH94uFL58/0l8DeCSyk+Aa6Pc95w98PAI+7+zWFkTUregyjJ78I45C0Nqcz7PFAdGBP+fnvF3Z8vpWOXKj0WNQXM7CyCf8FOdfeDLhyy4FY8rYDZ7r4h+ekKzVDsvEV8vjxwObDT3WeVdr5Czqe8SaS8yaW8yaW8yXWkeQ9y3AyS8LswWXmT5WjLW9pUpIqIiIhI7OiaVBERERGJHRWpIiIiIhI7KlJFRI4iFtysv8SP4RQROdpodb+ISAyY2QsED47YXGDXeUC6u+fdVqwdwX1wO5nZeUB54ETgTHf/Y6ryiogkm0ZSRUTiYTdwh7tnJr6Af7D/E8RuAfKK0T7ASQS3xboqlWFFRJJNRaqISDwUeT9hd98N+TfhbuPusyx4zv35BDf/3gRsMrPzUxNVRCT5NN0vIhIffzKzgtP9pwGETzd6Ash7ItMfgYcTntd9F8EN0C8OH5ErInJUU5EqIhIP5Qmm+7MSG83ss3CzI7AUqGNmvyZ4Ck9TM7sjsTswAhiQ/LgiIsmlm/mLiMSAmb0K/AjYUmBXc3evFvapQPAYzl8AnxE8WaiRuw8xsxHAHGBeOP0vInJU00iqiEg8nARc5u7rEhsTRlIJnxuPu88P950MrA13Vwa2qUAVkWOFFk6JiETMzKoAdQoWqMXQkWD0FKAqsLNUg4mIREhFqohI9G4B3i7YaGblgII37q8U7usFbHf3VWZWHmgAaBRVRI4ZKlJFRCJkZicSLHR6rED7/wAbgY8LfKRqeCuqm4He4SjscuCfwOrkJxYRSQ0tnBIRiZiZVXT33EP33O8z5voLXESOYSpSRURERCR2NN0vIiIiIrGjIlVEREREYkdFqoiIiIjEjopUEREREYkdFakiIiIiEjv/H2zy66HfLoPiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘图查看每月的活跃用户情况\n",
    "plt.figure(figsize=(10,4),dpi=80)\n",
    "x = monthly_active['InvoiceYearMonth'].astype('str').tolist()\n",
    "y = monthly_active['CustomerID'].tolist()\n",
    "plt.bar(x,y)\n",
    "plt.title('每月活跃用户数')\n",
    "plt.xlabel('月份')\n",
    "plt.ylabel('用户数')\n",
    "for a,b in zip(x, y):\n",
    "    plt.text(a, b+0.1, b, ha='center', va='bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4月份，月活跃客户数量从880个降至784"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**月订单数量**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_sales = uk.groupby('InvoiceYearMonth')['Quantity'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘图查看每月的订单数量\n",
    "plt.figure(figsize=(10,4),dpi=80)\n",
    "x = monthly_sales['InvoiceYearMonth'].astype('str').tolist()\n",
    "y = monthly_sales['Quantity'].tolist()\n",
    "plt.bar(x,y)\n",
    "plt.title('月订单数量')\n",
    "plt.xlabel('月份')\n",
    "plt.ylabel('订单数')\n",
    "\n",
    "for a,b in zip(x, y):\n",
    "    plt.text(a, b+0.1, b, ha='center', va='bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4月份的订单数量也下降了275k到259k,月活用户和月订单数量都下降了，看看平均订单收入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**月平均订单收入**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_order_avg = uk.groupby('InvoiceYearMonth')['Revenue'].mean().reset_index()\n",
    "\n",
    "\n",
    "x = monthly_order_avg['InvoiceYearMonth'].astype('str').tolist()\n",
    "y = monthly_order_avg['Revenue'].round(1).tolist()\n",
    "plt.figure(figsize=(10,4),dpi=80)\n",
    "plt.bar(x,y)\n",
    "plt.title('月平均订单收入')\n",
    "\n",
    "for a,b in zip(x, y):\n",
    "    plt.text(a, b+0.1, b, ha='center', va='bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "甚至4月份的月平均订单量稍微有所提升  \n",
    "新客户比例：如果我们正在失去现有的客户或无法吸引新的客户  \n",
    "留存率：我们看看基于同期群的用户留存情况  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**新客户比例**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在我们的数据集中，我们可以假设新客户是在我们定义的时间窗口中进行第一次购买的人"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#创建包含第一次购买时间和顾客id的DtaFrame\n",
    "min_purchase = uk.groupby('CustomerID').InvoiceDate.min().reset_index()\n",
    "min_purchase.columns = ['CustomerID','MinPurchaseDate']\n",
    "min_purchase['MinPurchaseYearMonth'] = min_purchase['MinPurchaseDate'].map(lambda date: 100*date.year + date.month)\n",
    " \n",
    "#合并min_purchase和之前的uk表\n",
    "uk = pd.merge(uk, min_purchase, on='CustomerID')\n",
    " \n",
    "uk.head()\n",
    " \n",
    "#创建一个字段来区分是新客户还是老客户\n",
    "uk['UserType'] = 'New'\n",
    "uk.loc[uk['InvoiceYearMonth']>uk['MinPurchaseYearMonth'],'UserType'] = 'Existing'\n",
    "#计算新老客户的每月消费\n",
    "user_type_revenue = uk.groupby(['InvoiceYearMonth','UserType'])['Revenue'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceYearMonth</th>\n",
       "      <th>UserType</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201101</td>\n",
       "      <td>Existing</td>\n",
       "      <td>199116.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201101</td>\n",
       "      <td>New</td>\n",
       "      <td>241759.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201102</td>\n",
       "      <td>Existing</td>\n",
       "      <td>219000.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201102</td>\n",
       "      <td>New</td>\n",
       "      <td>135617.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201103</td>\n",
       "      <td>Existing</td>\n",
       "      <td>295924.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201103</td>\n",
       "      <td>New</td>\n",
       "      <td>169860.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>201104</td>\n",
       "      <td>Existing</td>\n",
       "      <td>298686.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>201104</td>\n",
       "      <td>New</td>\n",
       "      <td>110046.951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>201105</td>\n",
       "      <td>Existing</td>\n",
       "      <td>455665.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>201105</td>\n",
       "      <td>New</td>\n",
       "      <td>94694.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>201106</td>\n",
       "      <td>Existing</td>\n",
       "      <td>416489.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>201106</td>\n",
       "      <td>New</td>\n",
       "      <td>107286.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>201107</td>\n",
       "      <td>Existing</td>\n",
       "      <td>423196.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>201107</td>\n",
       "      <td>New</td>\n",
       "      <td>61348.671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>201108</td>\n",
       "      <td>Existing</td>\n",
       "      <td>440660.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>201108</td>\n",
       "      <td>New</td>\n",
       "      <td>56534.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>201109</td>\n",
       "      <td>Existing</td>\n",
       "      <td>657706.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>201109</td>\n",
       "      <td>New</td>\n",
       "      <td>137100.541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>201110</td>\n",
       "      <td>Existing</td>\n",
       "      <td>682877.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>201110</td>\n",
       "      <td>New</td>\n",
       "      <td>138342.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>201111</td>\n",
       "      <td>Existing</td>\n",
       "      <td>857715.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>201111</td>\n",
       "      <td>New</td>\n",
       "      <td>117535.480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    InvoiceYearMonth  UserType     Revenue\n",
       "1             201101  Existing  199116.690\n",
       "2             201101       New  241759.640\n",
       "3             201102  Existing  219000.380\n",
       "4             201102       New  135617.820\n",
       "5             201103  Existing  295924.110\n",
       "6             201103       New  169860.080\n",
       "7             201104  Existing  298686.160\n",
       "8             201104       New  110046.951\n",
       "9             201105  Existing  455665.250\n",
       "10            201105       New   94694.100\n",
       "11            201106  Existing  416489.520\n",
       "12            201106       New  107286.070\n",
       "13            201107  Existing  423196.920\n",
       "14            201107       New   61348.671\n",
       "15            201108  Existing  440660.550\n",
       "16            201108       New   56534.360\n",
       "17            201109  Existing  657706.151\n",
       "18            201109       New  137100.541\n",
       "19            201110  Existing  682877.480\n",
       "20            201110       New  138342.650\n",
       "21            201111  Existing  857715.910\n",
       "22            201111       New  117535.480"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_type_revenuetmp = user_type_revenue.query('InvoiceYearMonth != 201012 and InvoiceYearMonth != 201112')\n",
    "user_type_revenuetmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a819374748>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = user_type_revenuetmp['InvoiceYearMonth'].astype('str').unique().tolist()\n",
    "y1 = user_type_revenuetmp.query(\"UserType == 'New'\")['Revenue'].tolist()\n",
    "y2 = user_type_revenuetmp.query(\"UserType == 'Existing'\")['Revenue'].tolist()\n",
    "plt.figure(figsize=(10,4),dpi=80)\n",
    "plt.title(\"New VS Exsiting\")\n",
    "plt.plot(x,y1,marker='o',label='New',color='red')\n",
    "plt.plot(x,y2,marker='o',label='Existing',color='blue')\n",
    "plt.legend()\n",
    "#plt.plot(x,y1,x,y2,marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "虽然客户越来越多，但是新客户有下降的趋势，我们查看下新客户的比例情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceYearMonth</th>\n",
       "      <th>Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201101</td>\n",
       "      <td>1.238754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201102</td>\n",
       "      <td>1.002950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201103</td>\n",
       "      <td>0.908894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201104</td>\n",
       "      <td>0.546351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201105</td>\n",
       "      <td>0.362606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201106</td>\n",
       "      <td>0.317037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>201107</td>\n",
       "      <td>0.244928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>201108</td>\n",
       "      <td>0.203463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>201109</td>\n",
       "      <td>0.317241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>201110</td>\n",
       "      <td>0.357616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>201111</td>\n",
       "      <td>0.245861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>201112</td>\n",
       "      <td>0.064639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    InvoiceYearMonth     Ratio\n",
       "1             201101  1.238754\n",
       "2             201102  1.002950\n",
       "3             201103  0.908894\n",
       "4             201104  0.546351\n",
       "5             201105  0.362606\n",
       "6             201106  0.317037\n",
       "7             201107  0.244928\n",
       "8             201108  0.203463\n",
       "9             201109  0.317241\n",
       "10            201110  0.357616\n",
       "11            201111  0.245861\n",
       "12            201112  0.064639"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a dataframe that shows new user ratio - we also need to drop NA values (first month new user ratio is 0)\n",
    "user_ratio = uk.query(\"UserType == 'New'\").groupby(['InvoiceYearMonth'])['CustomerID'].nunique()/uk.query(\"UserType == 'Existing'\").groupby(['InvoiceYearMonth'])['CustomerID'].nunique() \n",
    "user_ratio = user_ratio.reset_index()\n",
    "user_ratio = user_ratio.dropna()\n",
    "user_ratio.columns=['InvoiceYearMonth','Ratio']\n",
    "user_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hFkpJkiQVUlOhjIjFEfFgRCyPiK4qM6Mj4saIuDcivhkRp9U3qiRJklrRsIUyIqYBk4ETgA8Ci6uMngU8k5mnAeeVZyVJkrSPq2UN5ZnATZm5E7gHeE2VuZ8BR0VEJ3AS8P1qC4yIBRGxaeDriSeeGGluSZIktYhaCuVhwEaAzEzgkCpz3wd+DswDpgM3VFtgZi7NzO6Br0MPPXRkqSVJktQyRtcws4VdS2Rnlbn3Azdn5pciIoDvR8SrMvM3RUNKkiSpddWyhnINcAZARBwLbK4ydwgwsXz5OOBwIIsGlCRJUmurpVDeCZwcEdcAtwEfioi5ETF3t7mPAK+LiK3AMuCi8n6XkiRJ2ocNu8k7M7dHxKnANODGzLy/ytxGYEp940mSJKnV1bIPJZm5Hbh9L2eRJElSG/JMOZIkSSrEQilJkqRCLJSSJEkqxEIpSZKkQiyUkiRJKsRCKUmSpEIslJIkSSrEQilJkqRCLJSSJEkqxEIpSZKkQiyUkiRJKsRCKUmSpEIslJIkSSrEQilJkqRCLJSSJEkqxEIpSZKkQiyUkiRJKsRCKUmSpEIslJIkSSrEQilJkqRCLJSSJEkqxEIpSZKkQiyUkiRJKsRCKUmSpEIslJIkSSpkdLMDSPuynp4e+vr6GDduHDfccANdXV0V50455RQOOOAAAI455hiuu+66RsaUJKkQC6W0l/T19bFmzRrWr1/PqlWr6Onp4dprr33O3FNPPUVmsmrVqsaHlCSpDtzkLe0lK1asYPbs2YwaNYqpU6eydu3ainNr167lxz/+Maeddhonnngiy5cvb3BSSZKKcQ2ltJds3bqV8ePHAxARbNu2reJcd3c3y5cv5/jjj6e/v59zzjmH6dOnNzKqJEmF1LSGMiIWR8SDEbE8IirvBPbb2akRsSIioj4RpfbU2dm5S4ncsmVLxbmjjjqKV7/61QAce+yxPP744zz55JMNyShJUj0MWygjYhowGTgB+CCweIjZ3wE+BrwzM7NeIaV2NHnyZFauXAlAf39/1QNyLr300mcPwrn77rsZN24cBx98cMNySpJUVC1rKM8EbsrMncA9wGuGmP0ksAn444h4SbWhiFgQEZsGvp544okRhZbawYwZM1i3bh3z5s1j1qxZzJ8/n97eXnp7e3eZW7hwITfddBOvetWruPzyy7n11lubE1iSpD1Uyz6UhwEbATIzI+KQSkMRcRpwIjANOBi4KyJOy8xf7D6bmUuBpQPXu7u7XZupfU5HRwerV6+mr6+POXPmMGnSpIpzhx9+OF//+tcbnE6SpPqppVBuAQaXyM4qc5OAf8jMfoCI+B6lzeR3F0ootbGOjg5mzpzZ7BiSJO1VtWzyXgOcARARxwKbq8x9Hzi2PHcQcDzwSB0ySpIkqYXVUijvBE6OiGuA24APRcTciJi729xdwKaIuBf4V+AzmflwXdNKkiSp5Qy7yTszt0fEqZT2jbwxM++vMpfAwjrnkyRJUour6YPNM3M7cPteziJJkqQ25KkXJUmSVIiFUpIkSYVYKCVJklSIhVKSJEmFWCglSZJUiIVSkiRJhVgoJVXV09PDxIkTmT59Ops3VztJ1m8tWrSIyy67bO8HkzQk37tqNAulpIr6+vpYs2YN69evZ+HChfT09Aw5v3btWj7+8Y83KJ2kanzvqhkslJIqWrFiBbNnz2bUqFFMnTqVtWvXVp198sknueiii3j/+9/fwISSKvG9q2awUEqqaOvWrYwfPx6AiGDbtm1VZ//iL/6C+fPn093d3ah4kqrwvatmsFBKqqizs3OXP0RbtmypOPeVr3yFX/ziF5x33nmNiiZpCL531Qw1nctb0v5n8uTJrFy5kpkzZ9Lf309XV1fFuS984Qs88sgjTJkyhccee4zt27fzohe9iPe85z0NTiwJfO+qOSIzm52B7u7u3LRpU8Oeb8KiZQ17rpHYsOTsYWdaNTu0d/5askP75x+J7du3c+qpp3LyySdz7733cuGFFzJmzBgA5s6dW/Exvb29bNiwwaNFpSbyvau9JSIezcyK+0e4hlJSRR0dHaxevZq+vj7mzJnDpEmThn1MtT9WkhrH966awUIpqaqOjg5mzpzZ7BiSRsj3rhrNg3IkSZJUiIVSkiRJhVgoJUmSVIiFUpIkSYVYKCVJklSIhVKSJEmFWCglSZJUiIVSkiRJhVgoJUmSVIiFUtJ+b8eOHaxatYoHHnig2VHUZnztSCWeelHSPqunp4e+vj7GjRvHDTfcQFdX13NmMpNzzz2X448/nh/84Ae89KUv5eqrr25CWrUSXzvSyLiGUtI+qa+vjzVr1rB+/XoWLlxIT09PxblHHnmEWbNmccUVV/DZz36WO+64o8FJ1Wp87Ugj5xpKSfukFStWMHv2bEaNGsXUqVO56KKLKs4dffTRHH300fzkJz/h6quv5q1vfWuDk6rV+NqRRs41lJL2SVu3bmX8+PEARATbtm0bcv4b3/gGq1ev5iUveUkj4qmF+dqRRs5CKWmf1NnZuUsR2LJly5Dz559/Pl//+te55JJL9nY0tThfO9LI1VQoI2JxRDwYEcsj4rl7Jj93fklEXFY4nSTtocmTJ7Ny5UoA+vv7Kx5UAfDlL3+ZOXPmAPDLX/6Szs7OhmVUa/K1I43csIUyIqYBk4ETgA8Ci4eZfw1wYV3SSdIemjFjBuvWrWPevHnMmjWL+fPn09vbS29v7y5z06dP56CDDuKkk07i/PPP5/rrr29OYLUMXzvSyNVyUM6ZwE2ZuTMi7gE+Um0wIg4u33/lUMuOiAXAgoHrY8eOrTmwpD03YdGyZkeoaMOSs+u+zI6ODlavXk1fXx9z5sxh0qRJFedGjRrFpz71qbo/v9pXK7529qf3rtpTLYXyMGAjQGZmRBwyxOxVwIeAA4EJ1YYycymwdOB6d3d31hJWkkaio6ODmTNnNjuG2pCvHWlkatmHcgswuERW3EkkIv4n8ILMvKUewSRJktQeallDuQY4A7g9Io4FNleZeyPwsohYBYwDOiLiZ5n5iboklSRJUkuqpVDeCbw/Iq4BTgM+FBFzATKzd2AoM989cLl8/wTLpCRJ0r5v2EKZmdsj4lRgGnBjZt5fw2N665BNkiRJbaCmUy9m5nbg9r2cRZIkSW3IM+VIkiSpEAulJEmSCrFQSpIkqRALpSRJkgqxUEqSJKkQC6UktaCenh4mTpzI9OnT2by58vkknnnmGd72trdx+umnc+KJJ7JsWeuc77nd86u5fP20HwulJLWYvr4+1qxZw/r161m4cCE9PT1V517xilfw1a9+lZtvvpkFCxY0OGll7Z5fzeXrpz1ZKCWpxaxYsYLZs2czatQopk6dytq1ayvOzZgxg0suuQSAxx57jCOOOKKRMatq9/xqLl8/7ammDzaXJDXO1q1bGT9+PAARwbZt24ac3759OxdffDEf/ehHGxFvWO2eX83l66c9uYZSklpMZ2fnLn9Et2zZUnU2M3nHO97B+eefzymnnNKIeMNq9/xqLl8/7clCKUktZvLkyaxcuRKA/v5+urq6qs5efPHFvPCFL+R973tfo+INq93zq7l8/bQnC6UktZgZM2awbt065s2bx6xZs5g/fz69vb309vbuMnf33XfzsY99jG9/+9tMmTKFKVOm8Jvf/KY5oQdp9/xqLl8/7cl9KCW1jQmLWvNjQTYsObuuy+vo6GD16tX09fUxZ84cJk2aVHHuDW94Azt37qzrc9dDK+bfX147+4JWfP1oeBZKSWpBHR0dzJw5s9kx9li751dz+fppP27yliRJUiEWSkmSJBVioZQkSVIhFkpJkiQVYqGUJElSIRZKSZIkFWKhlCRJUiEWSkmSJBVioZQkSVIhFkpJkiQVYqGUJEmqk56eHiZOnMj06dPZvHlz1bnM5J3vfCe9vb2NC7cXWSglSZLqoK+vjzVr1rB+/XoWLlxIT09P1dnzzz+fb37zmw1Mt3dZKCVJkupgxYoVzJ49m1GjRjF16lTWrl1bdfaKK67gnHPOaWC6vctCKUmSVAdbt25l/PjxAEQE27Ztqzp71FFHNSpWQ1goJUmS6qCzs3OXErlly5YmpmmsmgplRCyOiAcjYnlEdFWZGR0Rn42Ir0bEAxFxdn2jSpIkta7JkyezcuVKAPr7++nqqliZ9knDFsqImAZMBk4APggsrjI6Dfh+Zp4OnA8srVdISZKkVjdjxgzWrVvHvHnzmDVrFvPnz6e3t3efOZJ7KKNrmDkTuCkzd0bEPcBHKg1l5p3AneWr44CfVltgRCwAFgxcHzt2bM2BJakdTVi0rNkRqtqwZPgNSq2av5bsaq5Wfe1A/V8/HR0drF69mr6+PubMmcOkSZOGnL/sssvq+vzNVEuhPAzYCJCZGRGHDDUcER3Ah4H3VpvJzKUMWoPZ3d2dNaWVJElqYR0dHcycObPZMRquln0otwCDS2RntcGICOAzwM2Z+Y2C2SRJktQGaimUa4AzACLiWKD6x76X1kw+nplX1yGbJEmS2kAthfJO4OSIuAa4DfhQRMyNiLmDhyLiDcCfA78XEavKX8+re2JJkiS1lGH3oczM7RFxKqWjuG/MzPurzN2Nn2spSZK036nloBwycztw+17OIkmSpDbkGkVJkiQVYqGUJElSIRZKSZIkFWKhlCRJUiEWSkmSJBVioZQkSVIhFkpJkiQVYqGUJElSIRZKSZIkFWKhlCRJUiEWSkmSJBVioZQkSVIho5sdQJIkqVVNWLSs2REq2rDk7GZH2IVrKCVJklSIhVKSJEmFWCglSZJUiIVSkiRJhVgoJUmSVIiFUpIkSYVYKCVJklSIhVKSJEmFWCglSZJUiIVSkiRJhVgoJUmSVIiFUpIkSYVYKCVJklSIhVKSJEmFWCglSZJUiIVSkiRJhdRUKCNicUQ8GBHLI6JriLn/FRHfjoivRsTL6xdTkiRJrWrYQhkR04DJwAnAB4HFVeaOAy4ETgbeBXy0fjElSZLUqiIzhx6I+DDw3cy8LiICeCgzj6swdzFweGb+7/L1bwMTM/M3FWYXAAsG3TQOeGzPv42mOhR4otkhCmjn/O2cHczfbO2cv52zg/mbrZ3zt3N2aP/8XZl5YKU7Rtfw4MOAjQCZmRFxyHBzZduALioUxcxcCiyt4blbXkRsyszuZufYU+2cv52zg/mbrZ3zt3N2MH+ztXP+ds4O7Z9/KLXsQ7kFGFwiO2ucOwyIPcwlSZKkNlFLoVwDnAEQEccCm2uY66S0GbvarCRJkvYRtRTKO4GTI+Ia4DbgQxExNyLmDh7KzAeAURHRCywDPp2Zz9Q5bytq90337Zy/nbOD+ZutnfO3c3Ywf7O1c/52zg7tn7+qYQ/KAYiIDmAa8Ghm3j/E3POAs4CnMvOrdUspSZKkllVToZQkSZKq8Uw5kiRJKsRCKUmSpEIslNpvRMSLI2JaRBzR7CySJO1L9vtCGRGHRsQdEXF3RDwQESdGxH+PiPsjYk1EvGvQbETEp3c/wj0iXhUR39zttornNY+IIyNiQ7tlj4hXRsTq8nOtjIgXtFn+44Dbgd8HVkTEpHbKP+i+QyLi4YiY0E75I+LYiHg0IlaVv97YLtkH3dcbEW8pkrsZ+SPiY4N+7msior/N8h8XEV8rP88dEXFwm+WfFBH/Uv4ebo6IQ1s0+3NmI6IrIu6N0u/+vymSuxn5q822S/6IeF1ErIvS39wvROkA6daVmfv1F/Ae4Lzy5dcDK4D7gNMpfTD7V4Dx5ftvAdYDcwc9/lRgObBh0G3HAd8DDgJeDvSVbz8WWAn8og2zXw8cU768GPjLNsv/NuDo8uU/B3raKf+g+/8eeBKY0E75gQuAee34vi3f9ybg9nbNP2hmEfCudsoPfAF4bfnyEuA9bZb/34CXlS//HXBpq2UfYvZzwNvLl28EJrfiz36ks22U/5+BF5Yv3zTwvK36td+voczMT2TmLeWr44CfAkdm5lez9K+4Aphavv+vgC/vtoiNwIzdbjuD0h+fpzLzh8ARUfpIpV8B51A6q1BbZc/Mt2fmw7s9Vzvl/yzwSEScCvwJ8MV2yg8QEW+gdDrTqh/d1cL5XwvMLf+PfnlEvLBdskdpF4lPAv0RMXvg36Nd8g/cWf6Znwt8uoIBh18AAAORSURBVM3yPwa8KiIOo1Ta/q2N8h8CjM3MR8ozG4Dnt2D2arNnUCo6UPpM6tcXyQ4Nz19tdo81Mn9m/lFmPr7bc7Ws/b5QDoiILqAH6GXXc5L/CngxQGb+ePfHZebGzPzNbjdXPK95Zj6WmU/VMzc0Jvug5zoVmAjcXI/s5WU2Kv8YSmWyo3x7XTQif0Q8H7gceHe9cg9o0M//c8CUzDyR0v/C39tG2f+2nH8p8CLqUMgGNPK9C1wMfCwzdxZPXtLA185rKb1mtgD13Hy5t/MfBnwjIi6JiLOA91Fa09Rq2SvOAtsH/c16drn10Ij81WbroUE//4HnOg/YkZlfKxh7rxrd7ACtICLGUPqldSnwLYqfk7xh5zVvZPaIeCnwKeAPM/PpPc08WCPzZ+avgT+PiD8D5gMX7WHsZzUw/0eBv87MxyPq91JqYP6Vmbm9fNu/ArP3KPAgDcw+CfiDzPx5RHwSeLjSg0eqwe/dMcAfA7+3p3l318D8HwOOz8wsl7LLKe22UkgD87+ZUiG+ELgvMx/c08wD9kL2agYXn7ott4H594pG5o+IiZTWXp5er2XuLfv9GsryJpVbgWWZ+fnM/FX59oH/iZ0APFLt8VU05LzmjcweEeOAL1Ha/+qHbZj/tog4ozzzAuC/2ij/VmAysCgiVlE6sOjWiDiqTfL/J/BgRLwoSm14FqW1lO2QfTPwfUr7PwOcAvyoSPby8hv9e2c68LU6/kewkfmPAF5Wfu28Dih8No5G5i+vjfompc3172vR7NV8NyJOqOdyG5y/7hqZPyL+B6V9V9+YmXXvEPXmGkp4B6Vfti+IiBmUfoFdCXw5Iu6j9Mf7gpEsMDMfiIiB85ofzd47r3nDskfEUkq/IC8vryH7l8xc3Eb5LwU+ExF/DfwceHvB7I3M/wRwzMBMuVTOzcwNbZL/6Yj4S+Be4NeU/vB+pE2yPxMRi4BPRMRfAZ3l5y6q0b93/oDSPnD10sif/0XAv1Da3eBbwHntlL989xXAlZn581bMPoSrgBsj4i5Ka7hfU4dlNjL/3tDI/J8BDgU+Vf67+w+Z+Zk6LbvuPPViFRHxCkr/o+zLzBEfRBNNPK95O2cvP7/5zb9H2jl7+fnNb/49UjT7EMudAJwM3JOZP6vXcis8z17J3yjtnr8eLJSSJEkqZL/fh1KSJEnFWCglSZJUiIVSkiRJhVgoJUmSVIiFUpIkSYX8f5D5WsCWSBO3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = user_ratio['InvoiceYearMonth'].astype('str').tolist()\n",
    "y = user_ratio['Ratio'].round(1).tolist()\n",
    "plt.figure(figsize=(10,4),dpi=80)\n",
    "plt.bar(x,y)\n",
    "plt.title('每月新客户比例')\n",
    "\n",
    "for a,b in zip(x, y):\n",
    "    plt.text(a, b+0.1, b, ha='center', va='bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每月的新客户比例在下降"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**群体每月留存情况**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([201101, 201102, 201103, 201104, 201105, 201106, 201107, 201108, 201109,\n",
       "       201110, 201111, 201112],\n",
       "      dtype='object', name='InvoiceYearMonth')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_purchase = uk.groupby(['CustomerID','InvoiceYearMonth'])['Revenue'].sum().reset_index()\n",
    "#创建crosstab\n",
    "retention = pd.crosstab(user_purchase['CustomerID'], user_purchase['InvoiceYearMonth']).reset_index()\n",
    "months = retention.columns[2:]\n",
    "new_column_names = [ 'm_' + str(column) for column in retention.columns]\n",
    "retention.columns = new_column_names\n",
    "\n",
    "#从第二个月开始取\n",
    "months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TotalUserCount</th>\n",
       "      <th>201101</th>\n",
       "      <th>201102</th>\n",
       "      <th>201103</th>\n",
       "      <th>201104</th>\n",
       "      <th>201105</th>\n",
       "      <th>201106</th>\n",
       "      <th>201107</th>\n",
       "      <th>201108</th>\n",
       "      <th>201109</th>\n",
       "      <th>201110</th>\n",
       "      <th>201111</th>\n",
       "      <th>201112</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InvoiceYearMonth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201101</th>\n",
       "      <td>647</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201102</th>\n",
       "      <td>679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201103</th>\n",
       "      <td>880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201104</th>\n",
       "      <td>784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201105</th>\n",
       "      <td>962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201106</th>\n",
       "      <td>889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201107</th>\n",
       "      <td>859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201108</th>\n",
       "      <td>834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201109</th>\n",
       "      <td>1146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201110</th>\n",
       "      <td>1230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201111</th>\n",
       "      <td>1505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201112</th>\n",
       "      <td>560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  TotalUserCount  201101  201102  201103  201104  201105  \\\n",
       "InvoiceYearMonth                                                           \n",
       "201101                       647     1.0    0.36    0.23    0.17    0.15   \n",
       "201102                       679     NaN    1.00    0.37    0.22    0.18   \n",
       "201103                       880     NaN     NaN    1.00    0.31    0.22   \n",
       "201104                       784     NaN     NaN     NaN    1.00    0.43   \n",
       "201105                       962     NaN     NaN     NaN     NaN    1.00   \n",
       "201106                       889     NaN     NaN     NaN     NaN     NaN   \n",
       "201107                       859     NaN     NaN     NaN     NaN     NaN   \n",
       "201108                       834     NaN     NaN     NaN     NaN     NaN   \n",
       "201109                      1146     NaN     NaN     NaN     NaN     NaN   \n",
       "201110                      1230     NaN     NaN     NaN     NaN     NaN   \n",
       "201111                      1505     NaN     NaN     NaN     NaN     NaN   \n",
       "201112                       560     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "                  201106  201107  201108  201109  201110  201111  201112  \n",
       "InvoiceYearMonth                                                          \n",
       "201101              0.13    0.12    0.11    0.11    0.09    0.08    0.07  \n",
       "201102              0.15    0.13    0.12    0.11    0.09    0.09    0.07  \n",
       "201103              0.16    0.12    0.11    0.11    0.09    0.08    0.06  \n",
       "201104              0.26    0.19    0.16    0.14    0.12    0.11    0.08  \n",
       "201105              0.38    0.23    0.17    0.15    0.12    0.11    0.07  \n",
       "201106              1.00    0.38    0.24    0.19    0.15    0.13    0.09  \n",
       "201107               NaN    1.00    0.41    0.27    0.19    0.17    0.11  \n",
       "201108               NaN     NaN    1.00    0.47    0.27    0.22    0.13  \n",
       "201109               NaN     NaN     NaN    1.00    0.39    0.26    0.13  \n",
       "201110               NaN     NaN     NaN     NaN    1.00    0.45    0.17  \n",
       "201111               NaN     NaN     NaN     NaN     NaN    1.00    0.23  \n",
       "201112               NaN     NaN     NaN     NaN     NaN     NaN    1.00  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retention_array = []\n",
    "#从第一个取值到倒数第二个月，\n",
    "for i in range(len(months)):\n",
    "    retention_data = {}\n",
    "    #201101->201102->201103->201104->201105\n",
    "    selected_month = months[i]\n",
    "    #\n",
    "    prev_months = months[:i]\n",
    "    \n",
    "    next_months = months[i+1:]\n",
    "    for prev_month in prev_months:\n",
    "        retention_data[prev_month] = np.nan\n",
    "        \n",
    "    #CustomerID和YearMonth分组后的每一个月CustomerID合计\n",
    "    total_user_count =  retention_data['TotalUserCount'] = retention['m_' + str(selected_month)].sum()\n",
    "    #当月置为1\n",
    "    retention_data[selected_month] = 1 \n",
    "    \n",
    "    query = \"{} > 0\".format('m_' + str(selected_month))\n",
    "    \n",
    " \n",
    "    for next_month in next_months:\n",
    "        query = query + \" and {} > 0\".format(str('m_' + str(next_month)))\n",
    "        retention_data[next_month] = np.round(retention.query(query)['m_' + str(next_month)].sum()/total_user_count,2)\n",
    "    retention_array.append(retention_data)\n",
    "    \n",
    "retention = pd.DataFrame(retention_array)\n",
    "retention.index = months\n",
    " \n",
    "#showing new cohort based retention table\n",
    "retention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a8193c80f0>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#热力图\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.title('留存率')\n",
    "sns.heatmap(data=retention,annot=True,fmt='.0%',vmin=0.0,vmax=0.5,cmap=\"Greens_r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    2\n",
       "3    3\n",
       "4    3\n",
       "dtype: object"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
